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      Smartphone addiction, daily interruptions and self-reported productivity

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      a , * , b , c , **
      Addictive Behaviors Reports
      Elsevier

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          Abstract

          The advent of the smartphone has dramatically altered how we communicate, navigate, work and entertain ourselves. While the advantages of this new technology are clear, constant use may also bring negative consequences, such as a loss of productivity due to interruptions in work life. A link between smartphone overuse and loss of productivity has often been hypothesized, but empirical evidence on this question is scarce. The present study addressed this question by collecting self-report data from N = 262 participants, assessing private and work-related smartphone use, smartphone addiction and self-rated productivity. Our results indicate a moderate relationship between smartphone addiction and a self-reported decrease in productivity due to spending time on the smartphone during work, as well as with the number of work hours lost to smartphone use. Smartphone addiction was also related to a greater amount of leisure time spent on the smartphone and was strongly related to a negative impact of smartphone use on daily non-work related activities. These data support the idea that tendencies towards smartphone addiction and overt checking of the smartphone could result in less productivity both in the workplace and at home. Results are discussed in relation to productivity and technostress.

          Highlights

          • A link between smartphone use and loss of productivity is investigated for the first time.

          • A moderate relationship is seen between smartphone addiction and decreased work productivity.

          • Smartphone addiction scores are also perceived to negatively affect daily life.

          • Findings are discussed in relation to flow and technostress.

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          Most cited references29

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          Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model.

          Within the last two decades, many studies have addressed the clinical phenomenon of Internet-use disorders, with a particular focus on Internet-gaming disorder. Based on previous theoretical considerations and empirical findings, we suggest an Interaction of Person-Affect-Cognition-Execution (I-PACE) model of specific Internet-use disorders. The I-PACE model is a theoretical framework for the processes underlying the development and maintenance of an addictive use of certain Internet applications or sites promoting gaming, gambling, pornography viewing, shopping, or communication. The model is composed as a process model. Specific Internet-use disorders are considered to be the consequence of interactions between predisposing factors, such as neurobiological and psychological constitutions, moderators, such as coping styles and Internet-related cognitive biases, and mediators, such as affective and cognitive responses to situational triggers in combination with reduced executive functioning. Conditioning processes may strengthen these associations within an addiction process. Although the hypotheses regarding the mechanisms underlying the development and maintenance of specific Internet-use disorders, summarized in the I-PACE model, must be further tested empirically, implications for treatment interventions are suggested.
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            Development and Validation of a Smartphone Addiction Scale (SAS)

            Objective The aim of this study was to develop a self-diagnostic scale that could distinguish smartphone addicts based on the Korean self-diagnostic program for Internet addiction (K-scale) and the smartphone's own features. In addition, the reliability and validity of the smartphone addiction scale (SAS) was demonstrated. Methods A total of 197 participants were selected from Nov. 2011 to Jan. 2012 to accomplish a set of questionnaires, including SAS, K-scale, modified Kimberly Young Internet addiction test (Y-scale), visual analogue scale (VAS), and substance dependence and abuse diagnosis of DSM-IV. There were 64 males and 133 females, with ages ranging from 18 to 53 years (M = 26.06; SD = 5.96). Factor analysis, internal-consistency test, t-test, ANOVA, and correlation analysis were conducted to verify the reliability and validity of SAS. Results Based on the factor analysis results, the subscale “disturbance of reality testing” was removed, and six factors were left. The internal consistency and concurrent validity of SAS were verified (Cronbach's alpha = 0.967). SAS and its subscales were significantly correlated with K-scale and Y-scale. The VAS of each factor also showed a significant correlation with each subscale. In addition, differences were found in the job (p<0.05), education (p<0.05), and self-reported smartphone addiction scores (p<0.001) in SAS. Conclusions This study developed the first scale of the smartphone addiction aspect of the diagnostic manual. This scale was proven to be relatively reliable and valid.
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              Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults - a prospective cohort study

              Background Because of the quick development and widespread use of mobile phones, and their vast effect on communication and interactions, it is important to study possible negative health effects of mobile phone exposure. The overall aim of this study was to investigate whether there are associations between psychosocial aspects of mobile phone use and mental health symptoms in a prospective cohort of young adults. Methods The study group consisted of young adults 20-24 years old (n = 4156), who responded to a questionnaire at baseline and 1-year follow-up. Mobile phone exposure variables included frequency of use, but also more qualitative variables: demands on availability, perceived stressfulness of accessibility, being awakened at night by the mobile phone, and personal overuse of the mobile phone. Mental health outcomes included current stress, sleep disorders, and symptoms of depression. Prevalence ratios (PRs) were calculated for cross-sectional and prospective associations between exposure variables and mental health outcomes for men and women separately. Results There were cross-sectional associations between high compared to low mobile phone use and stress, sleep disturbances, and symptoms of depression for the men and women. When excluding respondents reporting mental health symptoms at baseline, high mobile phone use was associated with sleep disturbances and symptoms of depression for the men and symptoms of depression for the women at 1-year follow-up. All qualitative variables had cross-sectional associations with mental health outcomes. In prospective analysis, overuse was associated with stress and sleep disturbances for women, and high accessibility stress was associated with stress, sleep disturbances, and symptoms of depression for both men and women. Conclusions High frequency of mobile phone use at baseline was a risk factor for mental health outcomes at 1-year follow-up among the young adults. The risk for reporting mental health symptoms at follow-up was greatest among those who had perceived accessibility via mobile phones to be stressful. Public health prevention strategies focusing on attitudes could include information and advice, helping young adults to set limits for their own and others' accessibility.
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                Author and article information

                Contributors
                Journal
                Addict Behav Rep
                Addict Behav Rep
                Addictive Behaviors Reports
                Elsevier
                2352-8532
                19 July 2017
                December 2017
                19 July 2017
                : 6
                : 90-95
                Affiliations
                [a ]Department of Psychology, Goldsmiths, University of London, London, UK
                [b ]Institute of Psychology and Education, Ulm University, Ulm, Germany
                [c ]Key Laboratory for NeuroInformation / Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
                Author notes
                [* ]Corresponding author at: Department of Psychology, Goldsmiths, University of London, Lewisham Way, London, SE14 6NW.Department of Psychology, GoldsmithsUniversity of LondonLewisham WayLondonSE14 6NW e.duke@ 123456gold.ac.uk
                [** ]Corresponding author at: Institute of Psychology and Education, Molecular Psychology, Zentrum für Biomedizinische Forschung, Ulm University, Helmholtzstr. 8/1, D-89081 Ulm, Germany.Institute of Psychology and EducationMolecular PsychologyZentrum für Biomedizinische ForschungUlm UniversityHelmholtzstr. 8/1UlmD-89081Germany christian.montag@ 123456uni-ulm.de
                Article
                S2352-8532(17)30015-9
                10.1016/j.abrep.2017.07.002
                5800562
                29450241
                3d76c192-dbda-489e-9439-6824d3ba6e9f
                © 2017 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 3 April 2017
                : 18 July 2017
                : 18 July 2017
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